1. High Performance, Pipelined, FPGA-Based Genetic Algorithm Machine A Review Grayden Smith Ganga Floora 1

2. Outline Paper Summary Background on Genetic Algorithms Implementation Results Critique Conclusion 2

3. The paper in summary Genetic algorithms are a commonly used heuristic solution discovery technique Improving the speed at which genetic algorithms are run Explore a pipeline, custom hardware solution for running genetic algorithms quickly Make use of pipelining, and specific genetic algorithm technique choices Achieved a speedup of 9600x over comparable general purpose processors 3

4. Genetic algorithms Before exploring how to increase the performance of genetic algorithms, it is important to understand genetic algorithms Genetic algorithms mimic the process of natural selection to create progressively fitter solutions There are 4 main components of a genetic algorithm Population generation Fitness measurement Crossover Mutation In short form, a pool of possible solutions is created where the features of the solution are understood as “genetic code” Solutions are bred with other solutions, and superior solutions are added back to the pool for future stages This ends when an acceptable solution is reached 4

5. Genetic algorithm, psuedo-code 5

6. Algorithm design choices The type of genetic algorithm used in this design is called a steady-state, survival driven genetic algorithm This type of genetic algorithm was chosen specifically to make it easy to implement in hardware Population storage: The alternative is generational genetic algorithm However, a generational genetic algorithm would require a flexible memory space Parent Selection: The alternative would be to choose based on fitness Choosing based on fitness would necessitate added hardware in the memory stage 6

7. Algorithm design choices Crossover and mutation: Can easily rely on simple bit shift registers Survival Driven Evolution: Parents are not selected by fitness so the population will slowly become built only on survivors In short, this is a very simple genetic algorithm Tests were done using the Royal Road technique to verify validity 7

8. Genetic algorithm pipeline 8

9. Pipeline Datapath Bit-Slice 9

10. Crossover implementation 10

11. Mutation implementation 11

12. Real time control screen 12 Protein folding problem: real-time control screen. For a 50,000 crossover run, the screen is updated at a rate of 36.67 runs per second which allows real-time adjustment of the mutation and crossover parameters.

13. Example Problem: Protein Folding To discover the minimum-energy conformation for a lattice- constrained problem This problem is NP-hard The problem represents: Chain of amino acids/residues Divided into hydrophobic residues that are repelled by water and hydrophilic residues that can form hydrogen bonds with water molecules Hydrophobic residues become clustered in the center of hydrophilic residues 13

14. 14 Example Problem: Protein Folding

15. Optimization problem that models many resource selection problems and is vital for logic circuit minimization This problem is NP-hard Defined as: Have a collection C of finite sets, with non-negative cost Find minimum cost sub-collection C’ Every element within C belongs to at least one set in C’ 15 Example Problem: Set Covering

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17. 17 Example Problem: Set Covering Set-coverage fitness function circuit.

18. Example Problem: Set Covering 18

19. Performance 19 ProblemClock Rate LUTsSpeedupPopulation Size (chromosomes) Hardware Set Coverage (94x520) 1MHz--2200x256Aptix AXB-MP3 Protein Folding (512x82) 66MHz6000320x/9600x512Xilinx XCV300 FPGA/Xilinx XCV3200E FPGA

20. Critique The fitness function: Needs to be implemented for different problems Can it be fit nicely into one clock cycle? If new fitness functions can’t be implemented quickly, is it worth it? Should development costs be incurred for ASIC implementation? This is addressed to some degree in the paper This function would likely require a good deal of design time and effort Evolutionary stasis not explained: The pipeline has no apparent method for finding stasis Should it be a counter? A comparator? Pipeline data hazards: Not addressed anywhere Quite low risk, but can impact performance and results 20

21. Critique Paper results and evaluation: Limited information provided on: Resource usage Performance Little comparison between FPGA implementations However, good study of genetic algorithm behaviour Good dataflow description: Clear diagrams and description of implementation 21

22. Conclusion GA can be implemented onto FPGA technologies to help improve performance through speed-up Outlined the algorithm and the architecture that was used to demonstrate the claims for the research Demonstrated significant performance increase over conventional general purpose processing using a couple of different FPGA technologies Explains that the use of FPGA technology can be desirable for its speed but has limited flexibility when introducing new fitness functions More research needs to be conducted to allow for fast reconfiguration of fitness functions to maximize throughput 22

23. References [1]Shackleford, B., Snider, G., Carter, R. J., Okushi, E., Yasuda, M., Seo, K., & Yasuura, H. (2001). A high-performance, pipelined, FPGA-based genetic algorithm machine. Genetic Programming and Evolvable Machines, 2(1), 33-60 23